Humans are metal robots in a valid sense.

Mankind via neuroscience, computational biology, computational neuroscience and deep learning neural networks has been and is now awakening to the overwhelming evidence that we are all mechanical robots.

Abstract: Your cells contain liquified metal, sodium, potassium, calcium, ie charged ions, that allows your nerve cells to generate and respond to both voltage and electromagnetic waves. Increasingly deep learning neural networks are starting to create patterns, like artificial grid cells, that are surprising researchers with their functional similarities to how the brain is thought to work. Conclusion: if one sits with this research long enough the natural conclusion is that humans are metal robots.

The self aware neural network of your brain is predicting future video frames, future audio signals, predicting what you might touch, taste, smell, and these predictions resemble the patterns of the original incoming sensory signals.

What you are seeing right now is a video feed, in your brain, but I believe that it’s actual video, that your mind’s neural network is simulating the same kind of signal processing that humans use for actual video, except that in addition to creating video your mind is also learning from the video stream that it is producing. This idea is based on an understanding of how a neural network can emulate a signal processor. Note that I didn’t say anything about how the video would be distributed or assembled in the brain. I’m not suggesting a frame by frame video signal but rather a volumetric 3D video, like a hologram, but one in which any point can change at anytime independent of the concept of a video frame.

By William Lotter, Gabriel Kreiman, David Cox (Submitted on 28 May 2018)

Abstract: “While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neuronal systems. Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks may be more than superficial. However, while CNNs capture key properties of the average responses of cortical neurons, they fail to explain other properties of these neurons. For one, CNNs typically require large quantities of labeled input data for training. Our own brains, in contrast, rarely have access to this kind of supervision, so to the extent that representations are similar between CNNs and brains, this similarity must arise via different training paths. In addition, neurons in visual cortex produce complex time-varying responses even to static inputs, and they dynamically tune themselves to temporal regularities in the visual environment. We argue that these differences are clues to fundamental differences between the computations performed in the brain and in deep networks. To begin to close the gap, here we study the emergent properties of a previously-described recurrent generative network that is trained to predict future video frames in a self-supervised manner. Remarkably, the model is able to capture a wide variety of seemingly disparate phenomena observed in visual cortex, ranging from single unit response dynamics to complex perceptual motion illusions. These results suggest potentially deep connections between recurrent predictive neural network models and the brain, providing new leads that can enrich both fields.”

The metal ions in your cells separate over time in part as a result of proteins moving in response to external stimulous, an example would be proteins in your ganglia neurons flipping because they are being hit with photons in your eye, separation of ions occurs in part because of the exchange of ions via neurotransmitters transmitted between cells via synapses, separation occurs in part because of reactions to electro-magnetic brainwaves which may increase or decrease the electrical potential of a given location in the brain, changing the threshold for when a cell or dendrite may fire. When the positive and negative charges separate to a significant amount an action potential is triggered, either in the dendrite, or along the axon. Positive and negative ions are sent between cells, as neurotransmitters, changing the thresholds at which cells further down the line may fire. In addition, the synapses have a variety of other threshold mechanisms, and can for example become configured to only fire when certain conditions are met, such as when two or more signals (signals representing cells that fired their action potential) beneath them in a hierarchy are triggered within a configurable amount of time such as three miliseconds or two miliseconds, or one milisecond. The dynamics of cell firing and neural circuit firing is a much deeper topic than I haven’t even touched the surface of everything involved.

This book by Peter Tse dives deep into how the neurons are coincidence detectors and how they may process signals.

Point 3: The brain is digital, its not analog, it processes discreet information like a computer. A recent paper changes the dominant paradigm of neuroscience for at least the past 50 years.

See this paper: “Is Information in the Brain Represented in Continuous or Discrete Form?”

“James Tee, Desmond P. Taylor

“(Submitted on 4 May 2018)

“The question of continuous-versus-discrete information representation in the brain is a fundamental yet unresolved physiological question. Historically, most analyses assume a continuous representation without considering the alternative possibility of a discrete representation. Our work explores the plausibility of both representations, and answers the question from a communications engineering perspective. Drawing on the well-established Shannon’s communications theory, we posit that information in the brain is represented in a discrete form. Using a computer simulation, we show that information cannot be communicated reliably between neurons using a continuous representation, due to the presence of noise; neural information has to be in a discrete form. In addition, we designed 3 (human) behavioral experiments on probability estimation and analyzed the data using a novel discrete (quantized) model of probability. Under a discrete model of probability, two distinct probabilities (say, 0.57 and 0.58) are treated indifferently. We found that data from all participants were better fit to discrete models than continuous ones. Furthermore, we re-analyzed the data from a published (human) behavioral study on intertemporal choice using a novel discrete (quantized) model of intertemporal choice. Under such a model, two distinct time delays (say, 16 days and 17 days) are treated indifferently. We found corroborating results, showing that data from all participants were better fit to discrete models than continuous ones. In summary, all results reported here support our discrete hypothesis of information representation in the brain, which signifies a major demarcation from the current understanding of the brain’s physiology.”

Point 5: Neural Networks in the Neo Cortex may also mimic grid cells for spatial reasoning

Last winter Jeff Hawkins revealed new research which suggests that columns in the neocortex basically mimic grid cells, but to identify every object in our environment including it’s features, it’s physical properties, including it’s present orientation in space relative to everything else. A single column of neurons is capable of modeling at least millions of patterns.

Choices may also be made at the cellular level, an idea expounded upon in Peter Tse’s book the Neural Basis of Freewill: Criterial Causation (linked earlier in the article). I think it’s possible that every cell is making a choice in a sense, it’s firing threshold may represent the information it must collect in order to make a decison, and it may be involved in setting the choice settings (or firing thresholds) for other cells above and below its place in the hierarchy of the cortex.

In closing: Consider the Dendrite as an advanced data structure, and consider that dendritic computation may have vast temporal and spatial memory capabilities for setting, presetting, and holding decision making information criteria based on a vast capability to response in very complex ways to incoming signals by making very tiny changes to its structure, including changes to the hairs on the dendrite, that have a consequence of changing the ionic properties of the dendrite, with the ability to create it’s own action potentials that can accumulate in cell firings at the axon level. Dendritic computation may be the key to the function of neural circuits. An idea I touched on in the Neural Lace Podcast a few times. Briefly in Episode 2 and to a greater extent in Episode 4

Humans are metal robots in a valid sense. Imagine that its like the tv show Westworld except every human is also a robot. This is partly why one day we can have AR VR plug directly into our brains (wirelessly) skipping the headset. (Like Sword Art Online or The Matrix)